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Found 5 papers, 2 papers with code
Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle
The widespread use of artificial intelligence (AI) systems across various domains is increasingly highlighting issues related to algorithmic fairness, especially in high-stakes scenarios.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts.
Detecting Concept Drift With Neural Network Model Uncertainty
Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model.
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic.
DEAL: Deep Evidential Active Learning for Image Classification
By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training.
